A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization
Stochastic gradient <span style="font-variant: small-caps;">sg</span>-based algorithms for Markov chain Monte Carlo sampling (<span style="font-variant: small-caps;">sgmcmc</span>) tackle large-scale Bayesian modeling problems by operating on mini-batches...
Saved in:
Main Authors: | Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi |
---|---|
Format: | article |
Language: | EN |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/ce4b43b8d50f4f07bbae919563d8ebfd |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo
by: Kaixian Yu, et al.
Published: (2021) -
Bayesian analysis of the genetic structure of a Brazilian popcorn germplasm using data from simple sequence repeats (SSR)
by: Saavedra,Javier, et al.
Published: (2013) -
Utilising Partial Momentum Refreshment in Separable Shadow Hamiltonian Hybrid Monte Carlo
by: Wilson Tsakane Mongwe, et al.
Published: (2021) -
Passive Control of Silane Diffusion for Gradient Application of Surface Properties
by: Riley L. Howard, et al.
Published: (2021) -
Bayesian validation framework for dynamic epidemic models
by: Sayan Dasgupta, et al.
Published: (2021)